factorization

This paper investigates whether tabular foundation models (TFMs) like TabPFN, TabICL, TabDPT, and TabFM produce predictions consistent with any joint distribution. It demonstrates that all evaluated TFMs violate both marginalization and factorization consistency for classification and regression, questioning their Bayesian inference claims.

This paper shows that matching a marginal Gaussian prior in factorized generative models does not prevent conditional style leakage, where style latents carry class information. Multiple remedies are explored, but the authors conclude that marginal statistics alone cannot certify class-invariance.

FocusMem introduces a latent memory interface for GUI agents that separates content retention, state-conditioned readout, and a trust gate to improve memory reliability. It consistently outperforms fixed-memory baselines across five GUI-agent benchmarks.

This paper proposes FaStR, a method that factorizes the transition kernel in reinforcement learning using CP decomposition into separate state, action, and next-state encoders, improving sample efficiency especially in high-dimensional locomotion tasks.

This paper proposes Native Factorized Weights for transformers, where every linear layer is trained as a product of two low-rank matrices from initialization. Experiments show a corpus-determined optimal rank that minimizes validation loss and a generalization band, outperforming dense baselines with fewer parameters.